DiaNet v2 deep learning based method for diabetes diagnosis using retinal images.

Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and accessible diagnostic methods are essential. Current diagnostic tests like fasting plasma glucose (FPG), oral glucose tolerance tests (OGTT), random plasma glucose (RPG), and hemoglobin A1c (HbA1c) have limitations, leading to misclassifications and discomfort for patients. The aim of this study is to enhance diabetes diagnosis accuracy by developing an improved predictive model using retinal images from the Qatari population, addressing the limitations of current diagnostic methods. This study explores an alternative approach involving retinal images, building upon the DiaNet model, the first deep learning model for diabetes detection based solely on retinal images. The newly proposed DiaNet v2 model is developed using a large dataset from Qatar Biobank (QBB) and Hamad Medical Corporation (HMC) covering wide range of pathologies in the the retinal images. Utilizing the most extensive collection of retinal images from the 5545 participants (2540 diabetic patients and 3005 control), DiaNet v2 is developed for diabetes diagnosis. DiaNet v2 achieves an impressive accuracy of over 92%, 93% sensitivity, and 91% specificity in distinguishing diabetic patients from the control group. Given the high prevalence of diabetes and the limitations of existing diagnostic methods in clinical setup, this study proposes an innovative solution. By leveraging a comprehensive retinal image dataset and applying advanced deep learning techniques, DiaNet v2 demonstrates a remarkable accuracy in diabetes diagnosis. This approach has the potential to revolutionize diabetes detection, providing a more accessible, non-invasive and accurate method for early intervention and treatment planning, particularly in regions with high diabetes rates like MENA.

Bilateral exudative retinal detachment, unusual pattern of uveitis with multiple sclerosis.

MS (Multiple sclerosis) associated uveitis used to have limited phenotypes. Bilateral exudative retinal detachment has never been recognized as a pattern of MS-associated uveitis. We are reporting a patient with multiple sclerosis who presented initially with the usual pattern of intermediate uveitis and later developed bilateral exudative retinal detachment.

Comparison of demographic, clinical and laboratory characteristics between first and second COVID-19 waves in a secondary care hospital in Qatar: a retrospective study.

OBJECTIVE: To compare the patient profile and outcomes in Qatar during the first and second waves of the COVID-19 pandemic. SETTING: A retrospective observational study was conducted comparing the demographic, clinical and laboratory characteristics of patients with COVID-19 infection admitted to a secondary care hospital, during the first and second waves of the pandemic. PARTICIPANTS: 1039 patients from the first wave and 991 from the second wave who had pneumonia on chest X-ray and had a confirmed SARS-CoV-2 infection by a real-time PCR test of a nasopharyngeal swab were included. Patients with a normal chest X-ray and those who had a negative PCR test despite a positive COVID-19 antigen test were excluded. OUTCOME: Length of stay, need for mechanical ventilation, final disposition and mortality were the key outcomes studied RESULTS: Influenza like symptoms (18.5% in the first wave vs 36.1% in the second wave, p 0.001), cough (79.2% vs 87%, p<0.001) and dyspnoea (27.5% vs 38% p<0.001) were more common in the second wave. Second wave patients had significantly higher respiratory rate, lower peripheral oxygen saturation, needed more supplemental oxygen and had higher incidence of pulmonary embolism. More patients received hydroxychloroquine and antibiotics during the first wave and more received steroids, antivirals and interleukin-1 antagonist during the second wave. The second wave had a shorter length of stay (14.58±7.75 vs 12.61±6.16, p<0.001) and more patients were discharged home (22% vs 10%, p<0.001). CONCLUSIONS: Patients who presented during the second wave of COVID-19 pandemic appeared to be more ill clinically and based on their laboratory parameters. They required shorter hospitalisation and were more likely to be discharged home. This could represent greater expertise in handling such patients that was acquired during the first wave as well as use of more appropriate and combination therapies during the second wave.